So I’m a programmer, and recently I shared some work I’d been really proud of with a few of my colleagues

It was a project I put a ton of time and effort into from the architecture to the little details. I was excited to get some feedback, but instead, the first thing they asked was “Which AI tool did you use for this?”

I’m not gonna lie, it kinda stung. I know AI’s everywhere right now, but this was all me just me coding and building something cool. It’s frustrating to have people assume it’s all AI instead of actual skill and effort.

Anyway, it’s made me realize I want to find a company that really values programmers and the craft of what we do a place where they know the difference between a shortcut and genuine work. I’m good at what I do and I want to be somewhere that actually sees that.

I'm trying to join more than one job offer now and I talked to many of my friends in the same field, most of whom told me to ride the router in the same direction as the AI and give me some tools to help me in interviews and organise my profile, such as Google's many tools and Deepseak, some tools that answer the answer the interview Hammer interview and tools

It started as a tool to help me find jobs and cut down on the countless hours each week I spent filling out applications. Pretty quickly friends and coworkers were asking if they could use it as well so I got some help and made it available to more people.

The goal is to level the playing field between employers and applicants. The tool doesn’t flood employers with applications (that would cost too much money anyway) instead the agent targets roles that match skills and experience that people already have.

There’s a couple other tools that can do auto apply through a chrome extension with varying results. However, users are also noticing we’re able to find a ton of remote jobs for them that they can’t find anywhere else. So you don’t even need to use auto apply (people have varying opinions about it) to find jobs you want to apply to. As an additional bonus we also added a job match score, optimizing for the likelihood a user will get an interview.

There’s 3 ways to use it:

1. ⁠⁠Have the AI Agent just find and apply a score to the jobs then you can manually apply for each job

2. ⁠⁠Same as above but you can task the AI agent to apply to jobs you select

3. ⁠⁠Full blown auto apply for jobs that are over 60% match (based on how likely you are to get an interview)

It’s as simple as uploading your resume and our AI agent does the rest. Plus it’s free to use and the paid tier gets you unlimited applies. It’s called SimpleApply

Context: I am 25 passout, not a lot of experience. Probably the AI slop batch myself. But have been working for an American startup. Our team is pretty lean which led to me taking a few interviews so early in my career.

I took an interview today for the position of an AI Engineer (New Grad). The candidate was from USA from a reputed college, CS + Math major a 2026 graduate. 4 internships on her resume with deep tech words being highlighted. Good gpa in her uni too. But she does not know what an HTTP call is????

I don't want to be mean but I assumed this is the bare minimum every new grad should know. It started with her not being able to answer basic questions about her internships. She wasn't aware of rag or vector databases at all. When asked about fine tuning (because it was mentioned in her resume) she told me she suggested her team to shift from an LSTM model to XGBoost. And that is what she means by fine-tuning. I was so confused. I thought maybe I am making it hard so went to CS basic on what is TCP UDP just to check theory - no idea what these are. What is the difference between a HTTP call and a gRPC call - No clue what either of them are. I asked on how would she connect frontend and backend then, she says that's what I am eager to learn. Lastly I asked what databases do you have experience with. No idea at all. What dbs did your intern companies use? I don't know if they used any. I said thank you and cut the call. She had 4 mid to good looking interns despite all of this.

I am pretty sure it wasn't this bad when I was fresh out of college, I was clueless (I still am) but not this much. The sad part is this is the 3rd new grad interview I have taken in the past month, all of them were along the same lines. I know there are still people who are working hard and know their shit but I am just confused after today's interview.

Hey everyone!

I’m currently a 3rd-year CS undergrad specializing in Artificial Intelligence & Machine Learning. I’ve already covered a bunch of core programming concepts and tools, and now I’m looking for 4-5 like-minded and driven individuals to learn AI/ML deeply, collaborate on projects, and sharpen our coding and problem-solving skills together.

🔧 My current knowledge and experience:

• Proficient in Python and basics of Java.

• Completed DSA fundamentals and actively learning more

• Worked on OOP, web dev (HTML, CSS), and basic frontend + backend

• Familiar with tools like Git, GitHub, and frameworks like Flask, Pandas, Selenium, BeautifulSoup

• Completed DBMS basics with PostgreSQL

• Hands-on with APIs, JSON, file I/O, CSV, email/SMS automation

• Comfortable with math for AI: linear algebra, calculus, probability & stats basics and learning further.

• Interested in freelancing, finance tech, and building real-world AI-powered projects

👥 What I’m looking for:

• 4-5 passionate learners (students or self-learners) who are serious about growing in AI/ML

• People interested in group learning, project building, and regular coding sessions (DSA/CP)

• A casual but consistent environment to motivate, collaborate, and level up together

Whether you’re just getting started or already knee-deep in ML, let’s learn from and support each other!
We can form a Discord or WhatsApp group and plan weekly meetups or check-ins.

Drop a comment or DM me if you're in – let’s build something awesome together! 💻🧠

It started as a tool to help me find jobs and cut down on the countless hours each week I spent filling out applications. Pretty quickly friends and coworkers were asking if they could use it as well, so I made it available to more people.

How It Works:

1. Manual Mode – View your personal job matches with their score and apply yourself

2. Semi-Auto Mode – You pick the jobs, we fill and submit the forms

3. Full Auto Mode – We submit to every role with a ≥60% match

Key Learnings 💡

• 1/3 of users prefer selecting specific jobs over full automation

• People want more listings, even if we can’t auto-apply so our all relevant jobs are shown to users

• We added an “interview likelihood” score to help you focus on the roles you’re most likely to land

Our Mission is to Level the playing field by targeting roles that match your skills and experience, no spray-and-pray

Feel free to dive in right now, SimpleApply is live for everyone. Try the free tier or upgrade for unlimited applies (with a money-back guarantee), then drop your thoughts below!

Two years ago I helped a friend prep for a Meta ML engineer loop. We did 200 LeetCode problems together. He was sharp, fast, would solve mediums in under twelve minutes. He didn't get the offer.

When the recruiter walked him through the debrief, the feedback was strange. The coding round had been "fine". Not a red flag, not a strong signal. What sank him was the system design round. The interviewer had given him an ambiguous problem about a recommender producing biased outputs and asked what he'd do in the next thirty minutes. He defaulted to architecture. The interviewer kept pulling him back: "what would you do first?"

I've been on the other side of that table for the past two years, running ML system design and behavioral rounds at Meta. What happened to my friend is happening to a lot of strong engineers, and most of them don't know why. They're prepping for a loop that doesn't exist anymore.

What changed: the algo round isn't dead, but its weight in hire/no-hire collapsed. The reason isn't that algorithms stopped mattering — it's that AI got too good at them. By 2025 GPT-class models could solve ~80% of LeetCode mediums first try. The skill "produce a clean implementation of two-sum in 12 minutes" stopped predicting anything useful. So companies shifted weight to what AI can't fake yet — judgment, system reasoning, AI-collaboration literacy, communication.

This shift wasn't announced. But anyone on a hiring committee in the past 18 months can tell you it happened. The debriefs sound different. The candidates who get strong-hire recs look different from the ones who got them in 2023.

What's actually being tested in 2026:

1. Reasoning under uncertainty. Deliberately ambiguous problems with no clean answer. The interviewer watches how you decompose, prioritize, name tradeoffs. Pattern-matching to a template = filtered out. Slowing down, asking questions, reasoning out loud = offer.

2. System design at production scale. Not "design Twitter" — "design Twitter with a 100ms latency budget, a degrading existing model, a downstream team you can't change, and a vendor you can't swap." Real constraints, real production decisions.

3. AI-collaboration literacy. New in the last 12 months. Can you tell when a model's output is plausible-but-wrong? Do you verify or trust? Becoming an explicit scoring dimension at Anthropic, OpenAI, increasingly Google/Meta.

4. Communication / thinking out loud. The candidates who get strong-hire reports almost always reason out loud constantly. Not narrating — *reasoning*. Making assumptions visible. Flagging uncertainty.

What an E5 Meta ML loop looks like now vs 2022:

2022: two LeetCode rounds, one broad system design, one ML theory round, one behavioral.

2026: one shorter coding round (pseudocode often fine for harder parts), one heavily-constrained system design round, one ML deep-dive on a system you've actually built, one meaningfully harder behavioral.

If I were prepping for a FAANG ML loop today, time allocation:

- 30% system design (real-system reasoning, not whiteboarding)
- 25% behavioral (real story practice, not STAR memorization)
- 20% ML depth on systems you've actually shipped
- 15% coding (~60–80 well-chosen mediums is enough; diminishing returns past that)
- 10% AI-collaboration practice (new — practice reasoning out loud while using AI tools)

The mindset shift: the interview is no longer testing what you know. It's testing how you reason.

LeetCode was about pattern recognition. Modern ML loops aren't. They're about reasoning through novel problems where the pattern doesn't exist. The candidates who get offers slow down when they don't know, ask clarifying questions, name assumptions, propose multiple approaches, pick one with explicit tradeoffs — out loud.

If you've been shipping production ML systems, you already have the skills. You just need to practice expressing them out loud, under pressure, in 45 minutes.

Grind less. Reason more. Talk out loud.

---

Full version with the worked example (Meta E5 round breakdown phase-by-phase) at gradientcast.com/insights/why-faang-killed-the-algo-round

---

With the new college batch about to begin and AI/ML becoming the new buzzword that excites everyone, I thought it would be the perfect time to share a roadmap that genuinely works. I began exploring this field back in my 2nd semester and was fortunate enough to secure an internship in the same domain.

This is the exact roadmap I followed. I’ve shared it with my juniors as well, and they found it extremely useful.

Step 1: Learn Python Fundamentals

Resource: YouTube 0 to 100 Python by Code With Harry

Before diving into machine learning or deep learning, having a solid grasp of Python is essential. This course gives you a good command of the basics and prepares you for what lies ahead.

Step 2: Master Key Python Libraries

Resource: YouTube One-shots of Pandas, NumPy, and Matplotlib by Krish Naik

These libraries are critical for data manipulation and visualization. They will be used extensively in your machine learning and data analysis tasks, so make sure you understand them well.

Step 3: Begin with Machine Learning

Resource: YouTube Machine Learning Playlist by Krish Naik (38 videos)

This playlist provides a balanced mix of theory and hands-on implementation. You’ll cover the most commonly used ML algorithms and build real models from scratch.

Step 4: Move to Deep Learning and Choose a Specialization

After completing machine learning, you’ll be ready for deep learning. At this stage, choose one of the two paths based on your interest:

Option A: NLP (Natural Language Processing) Resource: YouTube Deep Learning Playlist by Krish Naik (around 80–100 videos) This is suitable for those interested in working with language models, chatbots, and textual data.

Option B: Computer Vision with OpenCV Resource: YouTube 36-Hour OpenCV Bootcamp by FreeCodeCamp If you're more inclined towards image processing, drones, or self-driving cars, this bootcamp is a solid choice. You can also explore good courses on Udemy for deeper understanding.

Step 5: Learn MLOps The Production Phase

Once you’ve built and deployed models using platforms like Streamlit, it's time to understand how real-world systems work. MLOps is a crucial phase often ignored by beginners.

In MLOps, you'll learn:

Model monitoring and lifecycle management

Experiment tracking

Dockerization of ML models

CI/CD pipelines for automation

Tools like MLflow, Apache Airflow

Version control with Git and GitHub

This knowledge is essential if you aim to work in production-level environments.

got anything else in mind, feel free to dm me :)

Regards Ai Engineer intern

I have trying to get a machine learning job for almost 5 years now in this almost three years are after I joined first and current job which is not related to machine learning. I am feeling ashamed of myself when thinking about this. I am not interested in my current job and don't know if I am really worth getting a machine learning job. I am currently working on a mutual fund classifier project where I working on some SQL as well. I am questioning myself a lot, thoughts of me being deluding myself that I can do it. I don't know. Please tell me whether I even have a chance or not? Am I just an idiot who thinks he can do it?

After a month of job hunting, I finally landed a Data Scientist interview. Fingers crossed!

And let me just say, this damn job market!!

Sharing a little tip—I found this internship opportunity on EasyJob AI by searching for "machine learning" and checking the "Startup Only" box. Maybe startups are more in need of people?

Over the past four months, I’ve been interviewing for Machine Learning Engineer (MLE) and Applied Scientist (AS) roles. While preparing, I realized that high-quality, relevant resources are actually quite limited, so I wanted to share my own experience here. I’ll probably break this down into 2 or 3 posts. This is Part 1.

For context, out of Google, Meta, Amazon, Microsoft, Apple, TikTok, Pinterest, Airbnb, and Netflix—excluding the company I currently/previously worked for (~5 YOE in ML)—I interviewed at the rest. I received offers from all but one. The exact titles vary slightly depending on the company (MLE / Applied Scientist / Data Scientist - Modeling Track), but for the sake of this post, I will just refer to all of them as MLE.

When preparing for MLE interviews, you generally need to focus on a few key areas:

1. Coding

Preparing for this is not fundamentally different from standard Software Development Engineer (SDE) coding prep. You need to grind LeetCode. Some companies will require your code to compile, and you'll either need to write your own test cases or pass pre-written ones.

Standard best practices apply here (which you can find in any SWE interview guide):

• Ask clarification questions: Are there memory constraints? What is the input range/format?

• Communicate: Discuss your high-level approach and data structures with the interviewer before writing a single line of code.

• Structure: Have a clear mental outline of what each block of your code is doing.

• Iterate: If you can't think of the most optimal approach immediately, start with a brute-force working solution and then optimize it.

The MLE Twist: What makes coding slightly different for MLEs is that some companies have dedicated "ML Coding" rounds or ask more mathematically inclined questions. For example, you might be asked to design a sparse matrix (including addition, subtraction, multiplication). This requires you to actually understand how matrix operations work under the hood.

Some companies might even ask you to implement a specific algorithm from scratch (like kNN or k-means) or use a Deep Learning framework to implement a basic model, like Transformer matrix operations (though this is less common). My advice: For the most common interview questions, I was able to find them on PracHub

2. ML System Design

This is the biggest differentiator between SDE and MLE interviews. Almost every company will have one or two rounds dedicated to this.

Generally, you will be asked to design a solution for a real-world use case. Common examples include: How would you design the YouTube recommendation system? or Design the DoorDash search box / auto-suggestion feature. Because these questions have high ambiguity and rely heavily on practical experience, they are very difficult to bluff your way through.

Here is how I suggest preparing:

A. Daily Accumulation (Hands-on Experience)

If you have hands-on experience building end-to-end ML projects (defining the problem, finding features, modeling, offline evaluation, online deployment, monitoring, iteration, etc.), you are already halfway there. Regardless of the specific ML domain you are asked about, the overarching architectural framework is largely the same; you are just plugging in different components.

If you lack ML product experience, you should at least have offline model-building experience, and then supplement that by reading engineering blogs from tech companies (like Pinterest or Airbnb) to build your product sense.

This "accumulation" really shows during follow-up questions. For instance, in an NLP app, they might ask how you'd handle extremely long text inputs. For a recommendation app, they might ask how you'd conduct counterfactual evaluation. You build a sense for these answers by following the state-of-the-art (SOTA), reading papers, attending conferences, and listening to talks. This is hard to cram for.

B. Prep Materials

• Courses: If you have very little practical experience, look into ML System Design on Educative or Grokking the Machine Learning Interview. This will at least give you a baseline framework (e.g., knowing that "Search" is split into Information Retrieval and Ranking). If you already have solid CV/NLP/Search/RecSys experience, these courses might be too basic for you.

• Engineering Blogs: Read company blogs to understand the exact types of problems they face and how they solve them.

• YouTube: Good videos are rare, but they exist. For example, [this talk] is pretty solid.

C. Read Papers

For someone who already has a habit of reading research papers, there is no faster way to prepare. A good applied research paper tells a complete story: Problem -> Features -> Methodology -> Evaluation -> Online Results. This is exactly the flow of an ML Design interview. The list of "must-read" classic papers is actually quite short:

• Recommendation/Search (Most common): Deep Neural Networks for YouTube Recommendations. If you only have time to read one paper, eat, sleep, and breathe this one.

• Auto-completion: Gmail Smart Compose: Real-Time Assisted Writing.

• Option Generation: Smart Reply: Automated Response Suggestion for Email.

• Ranking/Personalization: Airbnb has several excellent papers on this.

D. My Preferred Framework for Answering

When tackling an ML Design question, I like to follow this structure:

1. Ask Clarification Questions: * What is the stage of the project? (If it's early stage, you must address the cold-start problem).

   • What is the traffic volume? (This impacts your engineering robustness and latency considerations).

   • Can I assume standard data is logged? (Usually yes, but always double-confirm).

2. Draw the High-Level Architecture: Set up the standard "cookie-cutter" structure. Briefly describe the main components:

   • Online: Data (online user data + database fetches), ML Service, ML Model Artifact, Logging, and Monitoring.

   • Offline: Feature processing, Modeling, Evaluation.

   • Tip: I highly recommend drawing this out for the interviewer. Since most of my interviews were remote, I had an iPad ready to draw the architecture live. It shows you are highly organized, and when you dive into the details, the interviewer knows exactly which component you are talking about.

3. Deep Dive into Implementation:

   • Features: Brainstorm what data you need. For recommendations, this is usually split into: Document features (views, text/video embeddings), User features (gender, geo-location [super important for local search], watch/search history), and Interaction features (distance, previously clicked/watched, matched text). You must know how to handle non-numerical data using text/image/ID embeddings.

   • Modeling (The Meat): Frame the problem first (Is this regression? Classification? Ranking?). How do you encode the inputs? What is the model architecture (e.g., for ranking: bi-encoder vs cross-encoder vs poly-encoder; for RecSys: two-tower)? Compare the pros and cons of different models (LR vs Random Forest; LSTM vs Transformer). Finally, how do you choose your loss function?

   • Evaluation: Know the standard offline metrics (Accuracy, AUC, F1, Precision/Recall, MSE) and NLP-specific metrics (Perplexity, BLEU, ROUGE, BERTScore).

   • The Rest: You usually won't spend too much time here, but touch on how different data is stored and how the data pipeline is designed.

The rest of the interview depends entirely on what specific areas the interviewer wants to drill into—and that relies on your deep knowledge. For example, how do you handle a search for restaurant chains? How do you handle exploratory search (e.g., "movies for family")? What if you have very little data? This is where you can start discussing Knowledge Graphs, Active Learning, Reinforcement Learning, GANs, etc.

If you have any interesting project idea in Agentic AI space that i can add to my resume then do tell.

https://docs.google.com/spreadsheets/d/1CvbLUYW0Lq_9jtlXb6kZAc-oTpI-TzPWX9Nk8mc2YJw/edit?usp=drivesdk

Hi guys, I'm graduating this year, and am looking for internships/job opportunities/contract based work in the AI ML domain.

I have been struggling to find something so far, as most companies are demanding some work experience for AI Engineering and related roles/specialization. (My main specialization is inference optimization, but I'm open to working anywhere in the domain.)

Open to relocating anywhere. Can start on site from December, and remote right now.

Also, I would appreciate any feedback:)

GitHub

Targeting swe, data science, ml, maybe even analyst/bioinformatics roles.

Machine Learning Scientist (L4/5) - Promotional Assets & Creative Technology
Netflix
Remote
$150,000 – $750,000
https://www.moaijobs.com/job/machine-learning-scientist-l4-5-promotional-assets-creative-technology-netflix-8753

Machine Learning Systems Engineer, Research Tools
Anthropic
San Francisco, CA | New York City, NY
$300,000 – $405,000
https://www.moaijobs.com/job/machine-learning-systems-engineer-research-tools-anthropic-9932

Ph.D. Intern – Machine Learning & Generative AI
EvenUp
Remote
$50.00 – $88.00 per hour
https://www.moaijobs.com/job/ph-d-intern-machine-learning-generative-ai-evenup-6383

AI Engineer & Researcher - Post-training
xAI
San Francisco & Palo Alto, CA
$180,000 – $440,000
https://www.moaijobs.com/job/ai-engineer-researcher-post-training-xai-3669

Staff Machine Learning Engineer, AI Translation
Airbnb
United States
$204,000 – $255,000
https://www.moaijobs.com/job/staff-machine-learning-engineer-ai-translation-airbnb-4675

AI Research Scientist - Monetization AI
Meta
New York, NY
$177,000 – $251,000
https://www.moaijobs.com/job/ai-research-scientist-monetization-ai-meta-4684

Machine Learning Research Scientist / Research Engineer, LLM Evaluation
ScaleAI
San Francisco, CA; Seattle, WA; New York, NY
$220,000 – $325,000
https://www.moaijobs.com/job/machine-learning-research-scientist-research-engineer-llm-evaluation-scaleai-3754

Staff Data Scientist, Algorithms, Operations Research/Customer Support
Airbnb
United States
$194,000 – $240,000
https://www.moaijobs.com/job/staff-data-scientist-algorithms-operations-research-customer-support-airbnb-7962

Machine Learning Engineer
Paypal
San Jose, California, United States of America
$169,500 – $291,500
https://www.moaijobs.com/job/machine-learning-engineer-paypal-7366

Senior Data Scientist, Internal AI & Data Science - Economics
Visa
Foster City, CA, US
$132,000 – $211,300
https://www.moaijobs.com/job/senior-data-scientist-internal-ai-data-science-economics-visa-5228

Sr. Staff Machine Learning Engineer, Homefeed
Pinterest
Remote
$176,924 – $364,254
https://www.moaijobs.com/job/sr-staff-machine-learning-engineer-homefeed-pinterest-2149

Research Scientist, Personalization & Post Training
DeepMind
Mountain View, California, US
$141,000 – $202,000
https://www.moaijobs.com/job/research-scientist-personalization-post-training-deepmind-7251

Machine Learning Engineer, AI Safety - LLM MLOps
NVIDIA
US, CA, Santa Clara
$148,000 – $235,750
https://www.moaijobs.com/job/machine-learning-engineer-ai-safety-llm-mlops-nvidia-3152

Data Scientist
Stripe
N/A
C$208,000 – C$312,000
https://www.moaijobs.com/job/data-scientist-stripe-3313

Technical Solutions Data Analyst II
Datadog
New York, New York, USA
$112,500 – $165,000
https://www.moaijobs.com/job/technical-solutions-data-analyst-ii-datadog-1844

Staff Machine Learning Engineer, NativeAds
Spotify
Remote
$215,136 – $307,337
https://www.moaijobs.com/job/staff-machine-learning-engineer-nativeads-spotify-7301

ML Engineer, II - Multi Modal Fusion
Torc
Ann Arbor, MI
$153,200 – $183,800
https://www.moaijobs.com/job/ml-engineer-ii-multi-modal-fusion-torc-7312

Lead Data Scientist
Strava
San Francisco, CA
$230,000 – $268,000
https://www.moaijobs.com/job/lead-data-scientist-strava-5979

I've been applying for ML/AI Engineer Intern roles as well as some DS intern roles for about a month, I get shortlisted sometimes but then mostly get ghosted for some reason. But most applications just don't get any sort of response back.
I've applied for more than 100+ jobs, but only got like 4-5 interviews. I'm currently working as a GenAI intern, but I don't think I can learn and grow here and so I'm still actively looking for internships elsewhere. Please review my resume or just about give me any tips for how I can job search better.

Appreciate any sort of response. Thank you!

Please Any one review my resume ?

Please it would help me a lot! Couldn't able to crack any intership don't know why

I’m a CS major from India in 2nd year with a good base in core math (calculus, linear algebra, stats, discrete math) and ML, along with DSA-type programming. I’m wondering if AI/ML is still worth pursuing for a high-paying, meaningful career—beyond just the generative AI hype. I’m more interested in real-world problem areas like data science, quant, decision intelligence, computational biology, etc. Is it worth investing in this direction or should i become a classic olf software engineering?

Looking for internships